Dynamic Scaling Research Articles

An efficient reducing mechanism for energy consumption in data center using hybrid consolidation techniques

The rise in internet user demand is a major factor in the expansion of infrastructure and the upsurge in energy use in cloud, colocation, and some business data centres. The advent of 5G has compounded the situation, as it substantially gives room for many new types of digital services, resulting in a need for richer consume a lot of energy when no scaling method is applied. Services such as mail, data storage and retrieval and other cloud services also require a lot of high energy consumption which eventually result into carbon(IV) oxide (CO2) emissions to the environment. This research therefore, focuses on lowering the energy usage of a data centre with heterogeneous power awareness either in an idle server state or high-performance state using a novel hybridized algorithm called “DyVoFesLoReMu”, comprising Dynamic Voltage Frequency Scaling (Dvfs) and a modified Local Regression Minimum Utilization (LrMu). A real dataset (workload) obtained online from PlanetLab consisting of hosts and Virtual Machines (VM) was simulated on a data center in CloudSim 3.0.3. Tool kit with preset parameters consisting of VM Allocation Policy and VM Selection Policy was used. The tool kit was utilised to create cloud infrastructure and simulate the essential features of a cloud environment. The Cloudsim was installed on Eclipse Integrated Development Environment (IDE) 2019 version on Windows 10 operating system. The hybridized algorithm was compared with other five (5) existing energy reducing algorithms and it was found to be more efficient with a range of 41-90% reduction in energy usage from the ten days workload traces and in comparison with the existing algorithms used for the simulation.

Coherent and Incoherent Ultrafast Dynamics in Colloidal Gold Nanorods.

The study of the mechanisms that control the ultrafast dynamics in gold nanoparticles is gaining more attention, as these nanomaterials can be used to create nanoarchitectures with outstanding optical properties. Here pump-probe and two-dimensional electronic spectroscopy have been synergistically employed to investigate the early ultrafast femtosecond processes following photoexcitation in colloidal gold nanorods with low aspect ratio. Complementary insights into the coherent plasmonic dynamics at the femtosecond time scale and incoherent hot electron dynamics over picosecond time scales have been obtained, including important information on the different sensitivity to the pump fluence of the longitudinal and transverse plasmons and their different contributions to the photoinduced broadening and shift.

Multiscale analysis of land use and land cover changes in Sri Lanka by remote sensing: the impacts of post-war infrastructure development in the last 20 Years (2002-2022)

On a worldwide scale, land use and land cover changes (LULCC) is a major driver of global change and biodiversity erosion. This study aims to monitor at the scale of Sri Lanka, LULCC and vegetation dynamics, to identify the main changes and their drivers. It places emphasis on multiscale geospatial monitoring with satellite data but also mobilises the interdisciplinary knowledge of a research programme. First, national scale data allows the identification of major changes with the MODIS NDVI dataset using Mann-Kendall tests and time profile analysis. A second local scale was mobilised with a systematic diachronic visual interpretation of high-resolution images on Google Earth. The third step, a macro-regional scale focus on the South and East coasts, used LANDSAT imagery (Stacked K-means) verified by field studies (botanical and vegetation analysis, and interviews). About 92.5% of the island of Sri Lanka is stable or presents no significant trends in the vegetation cover. 5% show a significant positive (greening) trend between 2000 and 2020 around the Puttalam lagoon, west of the Samanalawewa Reservoir, in the Udawalawe National Park, east and north of Ella, and the Nuwaragala Forest Reserve. Only about 2.5% (165,000 ha) of the Island shows a negative significant trend mainly in the periphery of Colombo and Trincomalee. The first driver is a set of national planning decisions in terms of infrastructure development (including urban growth, housing programmes and agricultural fields, and the management of protected areas). The second driver comes under rural dynamics with increasing tree density in rural landscapes over the Uva Province. Infrastructure development initiated by the state, creates both underlying conditions for other activities and negative impacts on coastal ecosystems, such as degradation of wetlands (including protected areas).

A Matrix Pencil Formulation for Nonconservative Realization of Scaling-Based Controllers for Feedforward-Like Systems

We develop a general matrix pencil based approach for efficient non-conservative realization of dual dynamic high-gain scaling based control designs for a class of uncertain feedforward-like nonlinear systems. It is shown that the control design can be cast into a set of matrix pencil based sub-problems capturing the detailed system structure, state dependence structure of uncertain terms, and precise roles of design freedoms in the context of detailed structures of Lyapunov inequalities. The design freedoms in the dynamic high-gain scaling based design are extracted in terms of eigenvalues of the formulated matrix pencil structures. It is seen that the proposed matrix pencil based approach greatly reduces design conservatism and algebraic complexity compared to prior results on dynamic high-gain scaling based control designs.

Dynamic Hypersphere Embedding Scale Against Adversarial Attacks

Learning robust features against adversarial attacks is a challenging task that requires highly complex models, especially on aerial images, because they are subject to environmental and adversarial changes. Embedding hypersphere normalization, along with adversarial settings, causes performance degradation and enables the feature to overlap. To address this, in this article, we propose a dynamic hypersphere embedding scale (DHS) method that remaps the normalized features to a relative scale to learn robust features. The proposed method combines the benefits of hypersphere embedding without scarifying softmax advantages. The DHS aggregates the normalized features and the non-normalized ones. It uses a hypersphere embedding to enforce maximum-margin to the features that yield shorter magnitude and utilizes a dynamic scale to avoid features overlapping in the case of adversarial attacks. We validate the DHS's effectiveness by embedding the adversarial training attacks such as Projected Gradient Descent (PGD), CW, and DeepFool. Empirical experiments revealed that the DHS improves the model performance by 12% when using the PGD attack, with less computation than legacy hypersphere models. Another set of experiments showed that the DHS does not obfuscate the gradient.

Deep neural network for learning wave scattering and interference of underwater acoustics

It is challenging to construct generalized physical models of underwater wave propagation owing to their complex physics and widely varying environmental parameters and dynamical scales. In this article, we present a deep convolutional recurrent autoencoder network (CRAN) for data-driven learning of complex underwater wave scattering and interference. We specifically consider the dynamics of underwater acoustic scattering from various non-uniform seamount shapes leading to complex wave interference patterns of back-scattered and forward-propagated waves. The CRAN consists of a convolutional autoencoder for learning low-dimensional system representation and a long short-term memory (LSTM)-based recurrent neural network for predicting system evolution in low dimensions. The convolutional autoencoder enables efficient dimension reduction of wave propagation by independently learning global and localized wave features. To improve the time horizon of wave dynamics prediction, we introduce an LSTM architecture with a single-shot learning mechanism and optimal time-delayed data embedding. On training the CRAN over 30 cases containing various seamount geometries and acoustic source frequencies, we can predict wave propagation up to a time horizon of 5 times the initiation sequence length for 15 out-of-training cases with a mean L2 error of approximately 10%. For selected out-of-training cases, the prediction time horizon could be increased to 6 times the initiation sequence length. Importantly, such predictions are obtained with physically consistent wave scattering and wave interference patterns and at 50% lower L2 error compared to routinely use standard LSTMs. These results demonstrate the potential of employing such deep neural networks for learning complex underwater ocean acoustic propagation physics.

Route to chaos and resonant triads interaction in a truncated rotating nonlinear shallow-water model.

The route to chaos and the phase dynamics of the large scales in a rotating shallow-water model have been rigorously examined through the construction of an autonomous five-mode Galerkin truncated system employing complex variables, useful in investigating how large/meso-scales are destabilized and how their dynamics evolves and transits to chaos. This investigation revealed two distinct transitions into chaotic behaviour as the level of energy introduced into the system was incrementally increased. The initial transition manifests through a succession of bifurcations that adhere to the established Feigenbaum sequence. Conversely, the subsequent transition, which emerges at elevated levels of injected energy, is marked by a pronounced shift from quasi-periodic states to chaotic regimes. The genesis of the first chaotic state is predominantly attributed to the preeminence of inertial forces in governing nonlinear interactions. The second chaotic state, however, arises from the augmented significance of free surface elevation in the dynamical process. A novel reformulation of the system, employing phase and amplitude representations for each truncated variable, elucidated that the phase components present a temporal piece-wise locking behaviour by maintaining a constant value for a protracted interval, preceding an abrupt transition characterised by a simple rotation of ±π, even as the amplitudes display chaotic behaviour. It was observed that the duration of phase stability diminishes with an increase in injected energy, culminating in the onset of chaos within the phase components at high energy levels. This phenomenon is attributed to the nonlinear term of the equations, wherein the phase components are introduced through linear combinations of triads encompassing disparate modes. When the locking durations vary across modes, the resultant dynamics is a stochastic interplay of multiple π phase shifts, generating a stochastic dynamic within the coupled phase triads, observable even at minimal energy injections.

Clinical efficacy and safety of Picamilon in patients with progressive chronic cerebral ischemia

To study the efficacy and safety of the drug Picamilon with various therapy regimens in patients with stage II chronic cerebral ischemia (CCI). An open cohort clinical study involved 50 patients diagnosed with stage II CCI aged 51 to 69 years (average age 62.2±8.98 years). Patients received Picamilon first parenterally 200 mg (100 mg/ml, 2 ml) intravenously for 10 days, then orally in 50 mg tablets 3 times a day for 60 days. The total duration of therapy in the group was 70 days. The study included 3 visits (before treatment, after completion of the course, 1.5 months after completion of treatment). The dynamics of cognitive status according to the Montreal Cognitive Assessment Scale, vegetative disorders according to the A.M. Vane scale, neurological disorders according to the A.I. Fedin scale, and sleep quality according to the Ya. I. Levin scale were compared. The state of cerebral blood flow and endothelium was studied before and after treatment: dopplerography of cranial vessels, assessment of the level of methylated forms of arginine (ADMA, MMA, SDMA) and their ratios. The registration of adverse events against the background of therapy and the tolerability of treatment by patients was also carried out. Against the background of Picamilon treatment, a significant positive dynamics of the MoCA scale results was observed in the general sample of patients, increasing in the delayed period (20.9, 24.6 and 25.9, p<0.0001 and p=0.0006); sleep normalization was observed in 55% of patients by Visit 2 and in 81% of patients by Visit 3 (p<0.0001 and p=0.0025). Improvement of neurological functions is noted in 84% of patients, the score on the Fedin A.I. scale significantly decreases after treatment from 17.4±9.34 to 8.06±6.84 (p<0.0001) and to 5.31±5.71 in the delayed period (p=0.0002). Normalization of vegetative status was observed in 38% of patients with stage II CCI, and in 60% of cases there was a decrease in the severity of vegetative dystonia syndrome (p=0.0001). Picamilon therapy has high efficacy in assessing clinical outcomes (100%), good tolerability in 98% of patients and is characterized by a favorable safety profile (in 92% of patients). Picamilon significantly affects the parameters of cerebral hemodynamics: increases the linear velocity of blood flow, reduces the thickness of the intima-media complex and the resistance index. It affects markers of NO metabolism and endothelial function: significantly reduces elevated levels of ADMA and ADMA/MMA and (ADMA+SDMA)/MMA ratios. The use of Picamilon is effective in patients with stage II CCI in the form of step therapy contributes to a significant regression of neurological deficit, cognitive impairment, improvement of sleep quality and autonomic function; improves vascular endothelial function, reduces the risk of cardiovascular complications. Picamilon is a pathogenetic therapy agent that prevents the progression of CCI.

An Energy-Efficient Wireless Power Receiver With One-Step Adiabatic-Bipolar-Supply Generating for Implantable Electrical Stimulation Applications.

This paper presents an energy-efficient wireless power receiver for implantable electrical stimulation applications, which can achieve one-step adiabatic bipolar-supply that is generated by a hybrid single-stage dual-output regulating (SSDOR) rectifiers. The structure using only four switches overcomes the disadvantages that the two output voltage values in the traditional dual-output rectifiers are close to each other. A constant-current (CC) controlled adiabatic dynamic voltage scaling (DVS) technique is proposed to minimize the voltage headroom of the stimulating drivers and improve the stimulation efficiency significantly. In addition, the receiver adopts only one general constant on-time (COT) low-frequency control to adjust the stimulation current, reducing both the power consumption and the complexity of the control circuits. The proposed receiver has been fabricated in a 0.18 μm BCD process with ±6 V voltage compliance and 2.5 mA maximum stimulating current. With a current range from ±1.5 mA to ±2.5 mA, the measured maximum average headroom voltage is only 80 mV and the peak total efficiency of the receiver is 85.6 %. The functionalities of the proposed receiver have been successfully verified through in vitro experiments.

Suicidal Thought Detection using Max Voting Ensemble Technique

Online social networking (SN) data presents a data stream that is rich in context and temporal information. It holds promise for predicting suicidal thoughts and behaviors. The fusion of SN data with machine learning algorithms offers a potential path forward. This research proposes a Max Voting Ensemble classifier model applied to a Reddit dataset for the identification of suicidal ideation. The preprocessing involves data cleansing, tokenization, and lemmatization. Additionally, TF-IDF and Word2Vec word embedding techniques are applied. Diverse machine learning algorithms, including Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Multinomial Naive Bayes (MNB), AdaBoost, and XGBoost, are implemented. The results of selected Machine Learning Classifiers (MLCs) are amalgamated using a Max Voting Ensemble classifier. The research findings clearly indicate that the Max Voting Ensemble classifier yields improved precision of 91.39% coupled with a substantial accuracy of 87.5%. The application of Ensembling Techniques (ET) to SN data holds the potential to address the complexities and modeling challenges inherent in predicting acute Suicidal Ideation within these dynamic time scales.

Energy saving of cluster computing by CPU frequency Tuning using genetic algorithm

Dynamic voltage and frequency scaling (DVFS) is a tool used primarily to decrease computer processor energy consumption by lowering its operational frequency. Their only downside is that they distract from the efficiency of parallel applications while operating on parallel platforms. In a heterogeneous cluster architecture, however, a genetic algorithm is being implemented and applied to model the best trade-off between energy-saving and parallel application performance degradation. The proposed algorithm selects the best frequency vector in order to accomplish these objectives by providing the same compromise. So, the objective function of the genetic algorithm at the same time gives limited energy consumption and minimum decreases in performance. The SimGrid simulator will be used for all experiments. The suggested algorithm saves the average energy by (20 %) and the application performance degrades to the limit (0.15 %).

Cutting-Edge Inference: Dynamic DNN Model Partitioning and Resource Scaling for Mobile AI

Cutting-Edge Inference: Dynamic DNN Model Partitioning and Resource Scaling for Mobile AI

Evaluation of three doses of oral trazodone and their impact on handling, activity, and physiological parameters in rabbits: a prospective, blinded, randomized cross-over study.

No study has determined the minimal effective dose of trazodone required to induce behavioral changes and its safety profile in rabbits. Therefore, this study aimed to determine the minimal effective dose of trazodone to improve compliance to handling, and to evaluate associated changes in motor activity, physiological and arterial blood gas parameters. Eight intact female New Zealand White rabbits (2-month-old; 1.66 ± 0.12 kg) were included in this prospective, blinded, randomized cross-over study. After a 10-day acclimation, rabbits randomly received placebo or trazodone 10, 20 or 30 mg/kg orally (PLAC, TRAZ10, TRAZ20, TRAZ30) with a 1-week wash-out period. Compliance scoring (dynamic interactive visual analog scale; DIVAS), activity levels measured with accelerometry (T0-T600), physiological parameters (temperature, heart, and respiratory rates), and arterial blood gas parameters (up to T240) were evaluated. Compliance scores, accelerometry, physiological and arterial blood gas parameters and hypoxemia prevalence (PaO2 <60 mmHg) were analyzed using linear mixed models and Chi-squared tests, respectively (P<0.05). When compared with PLAC, DIVAS scores were significantly higher at T80-120, T40-120 and T120-200 in TRAZ10, TRAZ20 and TRAZ30 post-administration, respectively. When compared with baseline, DIVAS scores were significantly higher from T80-160, T40-240 and T80-200 in TRAZ10, TRAZ20 and TRAZ30, respectively. All other parameters were not significantly different. In TRAZ30, hypoxemia was observed in 2/8 rabbits (P=0.104). In conclusion, oral trazodone improved rabbit compliance at all studied dosages, especially 20 mg/kg improved rabbit compliance without decreasing motor activity or causing hypoxemia.

Re-evaluation of CO&lt;sub&gt;2&lt;/sub&gt; storage capacity of depleted fractured-vuggy carbonate reservoir

&lt;p&gt;Confronting the dual crises of energy supply-demand imbalances and climate change, carbon neutrality emerges as a vital strategy for China in mitigating resource and environmental constraints, while fostering technological advancement and sustainable growth. In the context of extensive hydrocarbon exploitation, the CO&lt;sub&gt;2&lt;/sub&gt; storage capacity within depleted oil fields could be significantly underestimated in comparison to the prevalent practice of saline aquifer sequestration. In this study, we employ both theoretical and computational models to investigate the temporal (from microseconds to millennia) and spatial (spanning pore, Darcy, and hybrid scales) dynamics of CO&lt;sub&gt;2&lt;/sub&gt; trapping mechanisms in post-depletion carbonate reservoir with fractured-vuggy systems. The multiscale storage efficiency factor is obtained from simulation results and substituted into the existing analytical models for calculating CO&lt;sub&gt;2&lt;/sub&gt; storage volume in field cases, reappraising the carbon sequestration potential of fracture-vuggy carbonate. Drawing from comparative results, we discern that depleted carbonate can dissolve and mineralize more CO&lt;sub&gt;2&lt;/sub&gt; than saline layer, despite the storage volume can be considerably less. The annual storage capacity per well of two geological systems are comparable. Under unfavorable geological conditions, the minimum unit storage capacity of carbonate reservoir exceeds that of saline aquifer. The study's discoveries offer fresh perspectives on reliable and efficient CO&lt;sub&gt;2&lt;/sub&gt; geological storage, contributing to the reduction of atmospheric carbon emissions and advancing the utilization of underground resources and global energy transformation.&lt;/p&gt;

HigherNet-DST: Higher-Resolution Network With Dynamic Scale Training for Rooftop Delineation

HigherNet-DST: Higher-Resolution Network With Dynamic Scale Training for Rooftop Delineation

Study on Dynamic Scaling Behavior of the Villain-Lai-Das Sarma Equation Based on Numerically Stable Nueral Networks

Study on Dynamic Scaling Behavior of the Villain-Lai-Das Sarma Equation Based on Numerically Stable Nueral Networks

AMS-Net: Modeling Adaptive Multi-Granularity Spatio-Temporal Cues for Video Action Recognition.

Effective spatio-temporal modeling as a core of video representation learning is challenged by complex scale variations in spatio-temporal cues in videos, especially different visual tempos of actions and varying spatial sizes of moving objects. Most of the existing works handle complex spatio-temporal scale variations based on input-level or feature-level pyramid mechanisms, which, however, rely on expensive multistream architectures or explore multiscale spatio-temporal features in a fixed manner. To effectively capture complex scale dynamics of spatio-temporal cues in an efficient way, this article proposes a single-stream architecture (SS-Arch.) with single-input namely, adaptive multi-granularity spatio-temporal network (AMS-Net) to model adaptive multi-granularity (Multi-Gran.) Spatio-temporal cues for video action recognition. To this end, our AMS-Net proposes two core components, namely, competitive progressive temporal modeling (CPTM) block and collaborative spatio-temporal pyramid (CSTP) module. They, respectively, capture fine-grained temporal cues and fuse coarse-level spatio-temporal features in an adaptive manner. It admits that AMS-Net can handle subtle variations in visual tempos and fair-sized spatio-temporal dynamics in a unified architecture. Note that our AMS-Net can be flexibly instantiated based on existing deep convolutional neural networks (CNNs) with the proposed CPTM block and CSTP module. The experiments are conducted on eight video benchmarks, and the results show our AMS-Net establishes state-of-the-art (SOTA) performance on fine-grained action recognition (i.e., Diving48 and FineGym), while performing very competitively on widely used Something-Something and Kinetics.

A comprehensive study on combined organic fouling and gypsum scaling in reverse osmosis: Decoupling surface and bulk phenomena

Reverse osmosis (RO), as an energy efficient desalination technology that is critical to mitigate water scarcity, encounters feedwater containing both organic foulants and inorganic scalants. However, comparing with extensive studies on individual fouling or scaling, our knowledge of the behavior and mechanisms associated with combined organic fouling and mineral scaling is still lacking. Due to the potential occurrence of mineral formation in both bulk solution and on the membrane surface, a complete, mechanistic understanding of combined fouling and scaling requires decoupling of surface and bulk phenomena. Herein, our study employed a comprehensive investigation to delve into the intricate interplay of gypsum scaling and organic fouling in RO process. Our systematic approach is accomplished through three sets of experiments that include static experiments and two types of dynamic experiments (i.e., (1) combined fouling and scaling, and (2) gypsum scaling on foulant-conditioned membranes). A variety of model foulants including humic acid, alginate, bovine serum albumin (BSA), and lysozyme were used to investigate the effects of foulant type. Our results demonstrate that the behavior of combined organic fouling and gypsum scaling aligns more with that of gypsum scaling on foulant-conditioned membranes rather than static experiments where bulk nucleation occurs, indicating the predominance of surface nucleation in RO. BSA exhibited a remarkable hindering effect on gypsum scaling, whereas other foulants displayed an additive effect. The lack of scaling mitigation by lysozyme suggests that molecular properties of protein must play a role in regulating the behavior of combined fouling and scaling. Results from multiple characterization techniques reveal the foulant-scalant interactions by delineating the morphological and chemical features of the fouling/scaling layers. Our study not only elucidates the mechanisms of combined organic fouling and gypsum scaling but also sheds light on potential strategies for membrane scaling control in RO desalination.

Голод 1921–1922 гг. в Уральской губернии: причины и последствия

Introduction. The study analyzes a variety of newly discovered archival documents and materials for a comprehensive insight into the 1921–1922 mass famine in Uralsk Governorate. To facilitate this, the paper shall examine the causes, mitigation measures, and consequences of the famine in the designated region of present-day Kazakhstan. Materials. So, the study focuses on related documents from the State Archive of West Kazakhstan Region that have never been introduced into scientific discourse and received only fragmented attention. Results. The work provides a historiographic review on the topic under consideration, while some key research results are based on analytical insights into the archival papers. Our narrative centers around the causes and socio-demographic consequences, conditions of livestock breeding and agrarian industries, famine mitigation measures. Statistical data contained in the reports of governorate-level commissions prove most instrumental in evaluating the 1921–1922 famine’s scale and tracing negative socioeconomic dynamics. Conclusions. Uralsk Governorate happened to be a most famine-affected region of Kazakhstan, which was dramatically preceded (and paralleled) by harsh natural and climatic conditions, such as drought, crop failure, zud (jut), and livestock reduction. These were aggravated by poor agrarian development of the region and reduction of cultivated areas — only to be face the early twentieth century political cataclysms (e.g., Russian Civil War, increase of taxation, etc.). The famine’s consequences included increased mortality rates, population decline, dramatic fall of living standards, and large numbers of orphans and homeless children.

ВИВЧЕННЯ ЖИТТЯ БІЖЕНЦІВ ЗА ДОПОМОГОЮ ОСВІТНІХ ВІДЕОІГОР

In recent years, scholarly investigations have yielded the consensus that video games possess multifaceted attributes that extend beyond their conventional perception as mere sources of leisure, diversion, and entertainment. The immersive nature of video games has led to increased interest in their use as platforms for engaging in discourses across a diverse array of subject matters with the potential to reach a wider audience and generate meaningful impact. Combined with the sense of presence and ownership that allows players to be placed in the shoes of characters grappling with social or political issues, video games are converting into an effective and accessible tool for learning and engagement with pressing cultural, social and political issues. Current research aims to understand better the potential of educational video games to address these pressing problems, in this case, the refugee crisis, to evoke empathy and raise awareness by looking at the problem 'behind the scenes' with an audience that initially was not interested in the topic. This qualitative study (N=78) employed the narrative research method to examine experiential participatory learning with participants playing the educational video game Against All Odds developed by UNHCR. By immersing players in this virtual experience, this game was created to educate and encourage a greater understanding of the realities faced by refugees, the complexity, and dangers of the refugee experience, as well as the reasons behind their displacement. The promising results of the research show that a video game can address serious social issues by creating a simplified but still dynamic scale model of refugee reality. Participants reported an enjoyable gaming experience, newfound interest in the issue at hand, increased empathy toward refugees, and motivation to help people in need. The study illustrates that by putting players in the shoes of struggling characters, video games could make learning about these topics more engaging and, as a result, more accessible and appealing to a wider audience, including non-gamers and people previously disinterested in the topic. These results invite educators, researchers, and scholars from the fields of education, game studies, social sciences, and cultural studies to explore the potential of video games as a possible tool for engaging students, addressing social issues, raising awareness, and fostering empathy. The article also identifies several prospective domains for future research.